Hi Niurys, The simplest method to ensure a good bootstrap is often to simplify the data file by removing rows that should not be used for the modeling before running the bootstrap. Notably, if you exclude an entire subject either based on the ID column or another column, usually the boostrap will not work correctly.
I believe that most if not all tools generate the bootstrap with new ID column values (the ID is given a new sequential value based on sampling order). If you exclude an entire subject based on another column, the you will not have the expected number of subjects in the analysis because all the bootstrap tools that I know of don't account for exclusions with making the new data file. If this doesn't help, giving more info will help. (What tool are you using for bootstrap? What command line are you running? Can you share the model and a snippet of the data?) Thanks, Bill -----Original Message----- From: owner-nmus...@globomaxnm.com <owner-nmus...@globomaxnm.com> On Behalf Of Niurys.CS Sent: Friday, May 10, 2019 12:17 PM To: nmusers <nmusers@globomaxnm.com> Subject: [NMusers] bootstrap Dear nmusers, I have a big doubt. When I used the bootstrap to evaluate my model, I had some bugs. In my code I use IGNORE statements based on FLAGS for some outliers. I don't know if I remove these IGNORE statements, the bootstrap will run well. Can you give me some suggestions??????? Regards Niurys de Castro Suárez -- MSc Niurys de Castro Suárez Profesor Asistente Farmacometría Investigador Agregado Departamento Farmacia Instituto de Farmacia y Alimentos, Universidad de La Habana Cuba "Una estrella brilla en la hora de nuestro encuentro"
